In various fields, image restoration has received huge interest and many researchers introduce several image restoration techniques to restore hidden clear images from degraded images. Moreover, aforesaid approaches performances are estimated impartially remnants the huge confront that might delay the furthermore improvement of developed image restoration techniques. Hence, an efficient noisy pixel prediction on the basis of the image restoration is introduced that uses the Deep Convolutional Neural Network (DCNN) classifier to restore the input image from several noises, such as random noise as well as impulse noise. An Improved Harmony Search Algorithm (IHSA) is adopted to train the DCNN optimally based on minimum error. After identifying the noisy pixels, by exploiting the neuro-fuzzy system the enhancement of pixel is performed. Finally, the experimental analysis is performed and the image restoration performance on the basis of IHSA is analyzed based on the SDME, PSNR, and SSIM. Ultimately, the adopted model attains the maximum PSNR SSIM for images with random noise, as well as maximum SDME with impulse noise, correspondingly.
In the research community field, query optimization plays an important role to retrieve the important and the appropriate documents on the basis of query indexing. In the documents, using the query retrieval process the information is retrieved on the basis of the distance measured. Although several methods are present in the query processing scheme as well as indexing, extracting the matched as well as appropriate documents still outcomes in numerous confronts in the research community. Hence, to retrieve the appropriate documents competently an effective cluster-based inverted indexing model is adopted. By exploiting stop word removal and stemming approaches, unnecessary and redundant words are removed. By cluster-based inverted indexing approach, document indexing is carried out that is the integration of Possibilistic fuzzy c-means (PFCM) clustering approach to index the documents. For user queries, such as multigram queries or semantic queries, on basis of Bhattacharyya distance to generate an enhanced query outcome, query matching is processed. By exploiting the Pearson correlation coefficient, the query optimization is carried out and the appropriate documents are retrieved efficiently. The achievement of a developed cluster-based indexing approach is carried out in this paper. The developed cluster-based indexing approach performance is calculated by exploiting measures, namely precision, recall, as well as F-measure.
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